Leveraging crop yield forecasts using satellite information for early warning in Senegal
Agricultural losses driven by climate variability and anthropogenic pressures have severely impacted food security in Senegal. There is a crucial need to generate early warning signals for the upcoming season to enhance food security in response to the sudden climate shocks like drought. In this stu...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Elsevier
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/162863 |
| _version_ | 1855520045680033792 |
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| author | Panjwani, Shweta Jampani, Mahesh Sambou, Mame H. A. Amarnath, Giriraj |
| author_browse | Amarnath, Giriraj Jampani, Mahesh Panjwani, Shweta Sambou, Mame H. A. |
| author_facet | Panjwani, Shweta Jampani, Mahesh Sambou, Mame H. A. Amarnath, Giriraj |
| author_sort | Panjwani, Shweta |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Agricultural losses driven by climate variability and anthropogenic pressures have severely impacted food security in Senegal. There is a crucial need to generate early warning signals for the upcoming season to enhance food security in response to the sudden climate shocks like drought. In this study, we investigated the spatial distribution of maize and groundnut using factor analysis with a principal component approach. We aimed to identify suitable predictors of crop yields for the development of a seasonal yield prediction model. Subsequently, multi-regression analysis was performed to predict crop yield based on various combinations of satellite-derived vegetation and climate (rainfall) datasets as well as agronomic data from Senegal's 40 districts between 2010 and 2021. Studies revealed a strong correlation between seasonal rainfall (May to September) and crop yield: a 10–20 % decline in rainfall can lead to crop losses. The accuracy of the yield prediction model, built on the best performing scenarios for each district based on monsoon onset, duration, and planting time, exceeded 0.5 (Rsquared) for all districts when combining rainfall and normalized difference vegetation index (NDVI) data. The model prediction accuracy varied between 0.6 and 0.8 for major crop growing areas. The study emphasizes that refining the yield prediction model using machine learning techniques can improve its accuracy and enable its implementation in early warning systems. This enhanced capability could bolster Senegal's resilience to climate change by aiding decision-makers and planners in developing more effective strategies to ensure food security. |
| format | Journal Article |
| id | CGSpace162863 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1628632025-12-08T09:54:28Z Leveraging crop yield forecasts using satellite information for early warning in Senegal Panjwani, Shweta Jampani, Mahesh Sambou, Mame H. A. Amarnath, Giriraj crop yield yield forecasting early warning systems climate change food security crop production maize groundnuts satellite observation normalized difference vegetation index rainfall spatial distribution decision making strategies Agricultural losses driven by climate variability and anthropogenic pressures have severely impacted food security in Senegal. There is a crucial need to generate early warning signals for the upcoming season to enhance food security in response to the sudden climate shocks like drought. In this study, we investigated the spatial distribution of maize and groundnut using factor analysis with a principal component approach. We aimed to identify suitable predictors of crop yields for the development of a seasonal yield prediction model. Subsequently, multi-regression analysis was performed to predict crop yield based on various combinations of satellite-derived vegetation and climate (rainfall) datasets as well as agronomic data from Senegal's 40 districts between 2010 and 2021. Studies revealed a strong correlation between seasonal rainfall (May to September) and crop yield: a 10–20 % decline in rainfall can lead to crop losses. The accuracy of the yield prediction model, built on the best performing scenarios for each district based on monsoon onset, duration, and planting time, exceeded 0.5 (Rsquared) for all districts when combining rainfall and normalized difference vegetation index (NDVI) data. The model prediction accuracy varied between 0.6 and 0.8 for major crop growing areas. The study emphasizes that refining the yield prediction model using machine learning techniques can improve its accuracy and enable its implementation in early warning systems. This enhanced capability could bolster Senegal's resilience to climate change by aiding decision-makers and planners in developing more effective strategies to ensure food security. 2024-11 2024-11-29T05:23:55Z 2024-11-29T05:23:55Z Journal Article https://hdl.handle.net/10568/162863 en Open Access Elsevier Panjwani, Shweta; Jampani, Mahesh; Sambou, Mame H. A.; Amarnath, Giriraj. 2024. Leveraging crop yield forecasts using satellite information for early warning in Senegal. Climate Smart Agriculture, 1(2):100024. [doi: https://doi.org/10.1016/j.csag.2024.100024] |
| spellingShingle | crop yield yield forecasting early warning systems climate change food security crop production maize groundnuts satellite observation normalized difference vegetation index rainfall spatial distribution decision making strategies Panjwani, Shweta Jampani, Mahesh Sambou, Mame H. A. Amarnath, Giriraj Leveraging crop yield forecasts using satellite information for early warning in Senegal |
| title | Leveraging crop yield forecasts using satellite information for early warning in Senegal |
| title_full | Leveraging crop yield forecasts using satellite information for early warning in Senegal |
| title_fullStr | Leveraging crop yield forecasts using satellite information for early warning in Senegal |
| title_full_unstemmed | Leveraging crop yield forecasts using satellite information for early warning in Senegal |
| title_short | Leveraging crop yield forecasts using satellite information for early warning in Senegal |
| title_sort | leveraging crop yield forecasts using satellite information for early warning in senegal |
| topic | crop yield yield forecasting early warning systems climate change food security crop production maize groundnuts satellite observation normalized difference vegetation index rainfall spatial distribution decision making strategies |
| url | https://hdl.handle.net/10568/162863 |
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